Oulis pointed out that there is a great deal of interest in specific mechanisms relating to mental disorders and that these mechanisms should play a role in classification. Although specific mechanisms are important, more attention should be given to general theories. The following example from Salmon illustrates the difference.

This chapter examines philosophical issues surrounding the classification of mental disorders by the Diagnostic and Statistical Manual of Mental Disorders (DSM). In particular, the chapter focuses on issues concerning the relative merits of descriptive versus theoretical approaches to psychiatric classification and whether the DSM should classify natural kinds. These issues are presented with reference to the history of the DSM, which has been published regularly by the American Psychiatric Association since 1952 and is currently in its (...) fifth edition. While the first two editions of the DSM adopted a theoretical (psychoanalytic) and etiological approach to classification, subsequent editions of the DSM have adopted an atheoretical and purely descriptive (“neo-Kraepelinian”) approach. It is argued that largest problem with the DSM at present—viz., its failure to provide valid diagnostic categories—is directly related to the purely descriptive methodology championed by the DSM since the third edition of the DSM. In light of this problem, the chapter discusses the prospects of a theoretical and causal approach to psychiatric classification and critically examines the assumption that the DSM should classify natural kinds. (shrink)

Abstract Recently, some philosophers of psychiatry (viz., Rachel Cooper and Dominic Murphy) have analyzed the issue of psychiatric classification. This paper expands upon these analyses and seeks to demonstrate that a consideration of the history of the Diagnostic and Statistical Manual of Mental Disorders (DSM) can provide a rich and informative philosophical perspective for critically examining the issue of psychiatric classification. This case is intended to demonstrate the importance of history for philosophy of psychiatry, and more generally, (...) the potential benefits of historically-informed approaches to philosophy of science. (shrink)

English Abstract: The Antisocial personality disorder and several other psychiatric constructs are questioned and deconstructed in this review, that uses psychoanalytic approach to explain the nature of psychopathy and to give recommendations in this respect. -/- German Abstract: In vorliegender Rezension werden psychiatrische Konstrukte psychoanalytischer Bewertung unterzogen und dekonstruiert. Während die Entität „Antisoziale Persönlichkeitsstörung“ aufgrund ihrer Unwissenschaftlichkeit verworfen wird, besteht mentales Konstrukt „Psychopathie“ der Realitätsprüfung. In weiterem Verlauf der Rezension wird Versuch unternommen, das Phänomen der Psychopathie aufzuklären, um sie begrifflich (...) sowie gesundheitlich einzugrenzen. (shrink)

This dissertation is a contribution to the contemporary field of phenomenological psychopathology, or the phenomenological study of psychiatric disorders. The work proceeds with two major aims. The first is to show how a phenomenological approach can clarify and illuminate the nature of psychopathology—specifically those conditions typically labeled as major depressive disorder and bipolar disorder. The second is to show how engaging with psychopathological conditions can challenge and undermine many phenomenological presuppositions, especially phenomenology’s status as a transcendental philosophy and its (...) corresponding anti-naturalistic outlook. In the opening chapter, I articulate the three layers of the subject matter of phenomenological research—what I refer to as “existentials,” “modes,” and “prejudices.” As I argue, while each layer contributes to what we might call the “structure” of human existence, they do not do so in the same way, or to the same degree. Because phenomenological psychopathology—and applied phenomenology in general—aims to characterize how the structure of human existence can change and alter, it is paramount that these layers be adequately delineated and defined before investigating these changes. In chapters two through five, I conduct hermeneutic and phenomenological investigations of psychopathological phenomena typically labeled as major depressive disorder or bipolar disorder. These investigations address the affective aspects of depression and mania, and the embodied aspects of depression. In addition to clearly articulating the nature of these phenomena, I show how certain psychopathological conditions involve changes in the deepest or most fundamental layer of human existence—what I refer to as existentials. As I argue, many of the classical phenomenologists (including Husserl and Heidegger) believed that these structural features were necessary, unchanging, and universal. However, this presupposition is challenged through the examination of psychopathological and neuropathological conditions, undermining the status of phenomenology as a transcendental philosophy. While this challenge to classical phenomenology is only sketched in the early chapters, in chapters six and seven I develop it in more detail in order to achieve two distinct ends. In chapter six I argue that psychopathology and neuropathology not only challenge phenomenology’s status as a transcendental philosophy, but also supply a key to developing a phenomenological naturalism (which I contrast with a naturalized phenomenology). Phenomenological naturalism, as I articulate it, is a position in which phenomenology is not subsumed by the metaphysical and methodological framework of the natural sciences, but nonetheless maintains the capacity to investigate how the natural world stands independent of human subjectivity (and how events in the natural world can bring about changes in the most fundamental structures of human existence). In the seventh chapter I argue that a phenomenology in which existentials are contingent and variable rather than necessary and unchanging allows phenomenologists to contribute to new dimensional approaches to psychiatric classification. Rather than begin from distinct categories of disorder, these approaches begin from distinct core features of human existence. These features, referred to as either dimensions or constructs, can vary in degree and are studied in both normal and pathological forms. (shrink)

This paper addresses philosophical issues concerning whether mental disorders are natural kinds and how the DSM should classify mental disorders. I argue that some mental disorders (e.g., schizophrenia, depression) are natural kinds in the sense that they are natural classes constituted by a set of stable biological mechanisms. I subsequently argue that a theoretical and causal approach to classification would provide a superior method for classifying natural kinds than the purely descriptive approach adopted by the DSM (...) since DSM-III. My argument suggests that the DSM should classify natural kinds in order to provide predictively useful (i.e., projectable) diagnostic categories and that a causal approach to classification would provide a more promising method for formulating valid diagnostic categories. (shrink)

In this chapter, I provide an overview of phenomenological approaches to psychiatric classification. My aim is to encourage and facilitate philosophical debate over the best ways to classify psychiatric disorders. First, I articulate phenomenological critiques of the dominant approach to classification and diagnosis—i.e., the operational approach employed in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the International Classification of Diseases (ICD-10). Second, I describe the type or typification approach to psychiatric classification, (...) which I distinguish into three different versions: ideal types, essential types, and prototypes. I argue that despite their occasional conflation in the contemporary literature, there are important distinctions among these approaches. Third, I outline a new phenomenological-dimensional approach. I show how this approach, which starts from basic dimensions of human existence, allows us to investigate the full range of psychopathological conditions without accepting the validity of current diagnostic categories. (shrink)

A pressing need for interrater reliability in the diagnosis of mental disorders emerged during the mid-twentieth century, prompted in part by the development of diverse new treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition answered this need by introducing operationalized diagnostic criteria that were field-tested for interrater reliability. Unfortunately, the focus on reliability came at a time when the scientific understanding of mental disorders was embryonic and could not yield valid disease definitions. Based (...) on accreting problems with the current DSM-fourth edition (DSM-IV) classification, it is apparent that validity will not be achieved simply by refining criteria for existing disorders or by the addition of new disorders. Yet DSM-IV diagnostic criteria dominate thinking about mental disorders in clinical practice, research, treatment development, and law. As a result, the modernDSMsystem, intended to create a shared language, also creates epistemic blinders that impede progress toward valid diagnoses. Insights that are beginning to emerge from psychology, neuroscience, and genetics suggest possible strategies for moving forward. (shrink)

Recently there has been a trend of moving towards biological and neurocognitive based classifications of mental disorders that is motivated by a dissatisfaction with the syndrome-based classifications of mental disorders. The Research Domain Criteria (indicated with the acronym RDoC) represents a bold and systematic attempt to foster this advancement. However, RDoC faces theoretical and conceptual issues that need to be addressed. Some of these difficulties emerge when we reflect on the plausible reading of the slogan “mental disorders (...) are brain disorders”, that according to proponents of RDoC constitutes one of its main presuppositions. Some authors think that endorsing this idea commits RDoC to a form of biological reductionism. We offer empirical and theoretical considerations for concluding that the slogan above should not be read as a reductionist thesis. We argue, instead, that the slogan has a pragmatic function whose aim is to direct research in psychopathology. We show how this function might be captured in the framework of a Carnapian explication as a methodological tool for conceptual engineering. Thus, we argue that a charitable interpretation of the aims of the proponents of RDoC should be understood as an attempt at providing an explication of the concept of mental disorder in terms of brain disorder whose main goal is to provide a more precise and fruitful notion that is expected to have a beneficial impact on classification, research, and treatment of psychiatric conditions. (shrink)

In view of the publication of the DSM-V researchers were asked to discuss the theoretical implications of the definition of mental disorders. The reasons for the use, in the DSM-III, of the term disorder instead of disease are considered. The analysis of these reasons clarifies the distinction between the general definition of disorder and its implicit, technical meaning which arises from concrete use in DSM disorders. The characteristics and limits of this technical meaning are discussed and contrasted to (...) alternative definitions, like Wakefield’s harmful/dysfunction analysis. It is shown that Wakefield’s analysis faces internal theoretical problems in addition to practical limits for its acceptance in the DSM-V. In particular, it is shown that: a) the term dysfunction is not purely factual but intrinsically normative/evaluative; b) it is difficult to clarify what dysfunctions are in the psychiatric context (the dysfunctional mechanism involved being unknown in most cases and the use of evolutionary theory being even more problematic); c) the use of conceptual analysis and commonsense intuition to define dysfunctions leaves unsatisfied empiricists; d) it is unlikely that the authors of the DSM-V will accept Wakefield’s suggestion to revise the diagnostic criteria of any single DSM disorder in accordance with his analysis, because this is an excessively extensive change and also because this would probably reduce DSM reliability. In conclusion, it is pointed up in which sense DSM mental disorders have to be conceived as constructs, and that this undermines the realistic search for a clear-cut demarcation criterion between what is disorder and what is not. (shrink)

In psychiatry some disorders of cognition are distinguished from instances of normal cognitive functioning and from other disorders in virtue of their surface features rather than in virtue of the underlying mechanisms responsible for their occurrence. Aetiological considerations often cannot play a significant classificatory and diagnostic role, because there is no sufficient knowledge or consensus about the causal history of many psychiatric disorders. Moreover, it is not always possible to uniquely identify a pathological behaviour as the symptom (...) of a certain disorder, as disorders that are likely to differ both in their causal histories and in their overall manifestations may give rise to very similar patterns of behaviour. -/- Consider delusions as an example. It wouldn’t be correct to define delusions as those beliefs people form as a result of a neurobiological deficit and a hypothesis-evaluation deficit (as some versions of the two-factor theory of delusions suggest), because for some delusions no neurobiological deficit may be found, and reasoning biases and motivational factors may be contributors to the formation of the delusion (e.g. McKay et al., 2005). Moreover, it would be a mistake to define delusions as symptoms of schizophrenia alone, because they occur also in other disorders, including dementia, amnesia, and delusional disorders. Thus, aetiological considerations may appear in the description and analysis of delusions, but do not feature prominently in their definition. -/- In this paper I argue that the surface features used as criteria for the classification and diagnosis of disorders of cognition are often epistemic in character. I shall offer two examples: confabulations and delusions are defined as beliefs or narratives that fail to meet standards of accuracy and justification. Although classifications and diagnoses based on features of people’s observable behaviour are necessary at these early stages of neuropsychiatric research, given the variety of conditions in which certain phenomena appear, I shall attempt to show that current epistemic accounts of confabulations and delusions have limitations. Epistemic criteria can guide both research and clinical practice, but fail to provide sufficient conditions for the identification of delusions and confabulations, and fail to demarcate pathological from non-pathological narratives or beliefs. -/- Another limitation of current epistemic accounts – which I shall not address here – is the excessive focus on epistemic faults of confabulations and delusions at the expense of their epistemically neutral or advantageous features (see Bortolotti and Cox, 2009). This may lead to a misconception of delusions and confabulations, and to an oversimplification in the assessment of the needs of people who require clinical treatment for their psychotic symptoms. (shrink)

The aim of this study was to obtain a baseline for clinical application of electroencephalography (EEG) in children and to evaluate the EEG findings in children with various acute and chronic central nervous system disorders and nonepileptic events. EEG records of 250 patients were studied in Neurophysiology Departments of Queen Rania Al-Abdullah Hospital for Children in Jordan. For each patient who had undergone EEG the following data were recorded: age, sex, source of referral (inpatient or outpatient department), reason for (...) EEG, diagnostic impressions, clinical presentation, the result of the EEG examinations and clinical correlation between the seizure type and EEG finding. Males (55%) were slightly outnumbered than females. The majority of cases were between 6-12 years. Total of 63.2% of all referrals for EEG were from outpatient clinics while inpatients accounted for 36.8% of referred cases, with Pediatric Neurology Department referrals being the highest (28.8%). Majority of diagnosis at referral were suspected epilepsy (80%) with epileptiform EEG abnormalities in 32%. Over all 64% the EEG records were normal, including all EEG records of children with syncope and headache. We conclude that there are many unnecessary routine EEG recordings in children. Investigation of epilepsy and acute encephalopathies appear to be the most valuable indications for routine pediatric EEG. EEG can be helpful in classification of seizures. Finding a way to reduce EEG requests is recommended. (shrink)

A large part of the controversy surrounding the publication of DSM-5 stems from the possibility of replacing the purely descriptive approach to classification favored by the DSM since 1980. This paper examines the question of how mental disorders should be classified, focusing on the issue of whether the DSM should adopt a purely descriptive or theoretical approach. I argue that the DSM should replace its purely descriptive approach with a theoretical approach that integrates causal information into the DSM’s (...) descriptive diagnostic categories. The paper proceeds in three sections. In the first section, I examine the goals (viz., guiding treatment, facilitating research, and improving communication) associated with the DSM’s purely descriptive approach. In the second section, I suggest that the DSM’s purely descriptive approach is best suited for improving communication among mental health professionals; however, theoretical approaches would be superior for purposes of treatment and research. In the third section, I outline steps required to move the DSM towards a hybrid system of classification that can accommodate the benefits of descriptive and theoretical approaches, and I discuss how the DSM’s descriptive categories could be revised to incorporate theoretical information regarding the causes of disorders. I argue that the DSM should reconceive of its goals more narrowly such that it functions primarily as an epistemic hub that mediates among various contexts of use in which definitions of mental disorders appear. My analysis emphasizes the importance of pluralism as a methodological means for avoiding theoretical dogmatism and ensuring that the DSM is a reflexive and self-correcting manual. (shrink)

The article advances a new way of thinking about classifications in general and the classification of mental disorders in particular. By applying insights from measurement practice to the context of classification, I defend a notion of epistemic accuracy that allows one to evaluate and improve classifications by comparing different classifying methods to each other. Progress in classification arises from the mutual development of classification systems and classifying methods. Based on this notion of accuracy, the article (...) illustrates with an example how psychiatric classifications can be improved via circumscribed comparisons of different perspectives on mental disorders, without relying on complete models of their complex aetiology. When applying this strategy, the traditional opposition between symptom-based and causal approaches is of little consequence for making progress in the epistemic accuracy of psychiatric classification. (shrink)

The question of how psychiatric classifications are made up and to what they refer has attracted the attention of philosophers in recent years. In this paper, I review the claims of authors who discuss psychiatric classification in terms referring both to the philosophical tradition of natural kinds and to the sociological tradition of social constructionism — especially those of Ian Hacking and his critics. I examine both the ontological and the social aspects of what it means for something to (...) be a mental disorder, and how the ontological status of these disorders hinges on social causation. Finally, I conclude by suggesting a way in which the biological and the social may be reconciled in an integrative model of variation in psychiatric disorder. (shrink)

Contemporary psychiatry finds itself in the midst of a crisis of classification. The developments begun in the 1980s—with the third edition of the Diagnostic and Statistical Manual of Mental Disorders —successfully increased inter-rater reliability. However, these developments have done little to increase the predictive validity of our categories of disorder. A diagnosis based on DSM categories and criteria often fails to accurately anticipate course of illness or treatment response. In addition, there is little evidence that the DSM categories (...) link up with genetic findings, and even less evidence that they... (shrink)

Aims: The Research Diagnostic Criteria for Temporomandi¬bular Disorders (RDC/TMD) Axis I diagnostic algorithms were demonstrated to be reliable but below target sensitivity and specificity. Empirical data supported Axis I algorithm revisions that were valid. Axis II instruments were shown to be both reliable and valid. An international consensus workshop was convened to obtain recommendations and finalization of new Axis I diagnostic algorithms and new Axis II instruments. Methods: A comprehensive search of published TMD diagnostic literature was followed by review (...) and consensus via a formal structured process by a panel of experts for revision of the RDC/TMD. Results: The recommended Diagnostic Criteria for TMD (DC/TMD) Axis I protocol includes both a valid screener for pain diagnoses and valid criteria for the most common pain-related TMDs and for one intra-articular disorder. The Axis II protocol retains selected RDC/TMD screening instruments augmented with new instruments to better assess the interactions between pain and psychosocial functioning. A comprehensive classification system is also presented. Conclusion: The recommended evidence-based DC/TMD protocol is appropriate for use in both the clinical and research settings. Simple Axis I and II screening tests augmented by validated Axis I and Axis II instruments allow for identification of simple to complex TMD patients. (shrink)

The Axis II Work Group of the Task Force on Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. R. D. Hare et al describe an alternative to the approach taken in the DSM-III—Revised (DSM-III—R; American Psychiatric Association, 1987), namely, the revised Psychopathy Checklist. The authors (...) also discuss the multisite APD field trials designed to evaluate and compare 4 criteria sets: the DSM-III—R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th edition of the International Classification of Diseases (World Health Organization, 1990), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist. (PsycINFO Database Record (c) 2015 APA, all rights reserved). (shrink)

Attention Deficit Hyperactivity Disorder (ADHD) continues to be controversial with arguments for and against its veracity being waged by individuals representing a variety of disciplines from behavioral scientists to philosophers. Our perspective focuses on the epistemological underpinnings of what is now commonly known as ADHD. Its ignominious history and current disputes may stem from a "pessimistic" epistemology, meaning that truth is only the province of persons in authority and power. The authoritative organizations that govern the diagnostic labels and criteria are (...) the American Psychiatric Association and their Diagnostic and Statistical Manual and the World Health Organization that sponsors the International Classification of Disease. We contrast the pessimistic epistemology with criteria for truth from the scientific method. Although scientific scrutiny has been and is being applied subsequent to "authoritarian edicts" of the disorder, we opine that ADHD currently does not have status beyond that of the "hypothetical construct." Moreover, current brain-based causal models have failed to provide rigorous supporting data that comes from testing falsifiable hypotheses. (shrink)

Hume describes “mental geography” as the endeavor to know “the different operations of the mind, to separate them from each other, to class them under their proper heads, and to correct all that seeming disorder, in which they lie involved, when made the object of reflection and enquiry.” While much has been written about his geography of thought in Treatise Book 1, relatively little has been written about his geography of feeling in Books 2 and 3, with the result that (...) there has been considerable puzzlement and disagreement about the meaning and grounds of many of his central theses about the passions, action, and morals. My endeavor in this chapter is to remove some common sources of perplexity about his classification of the operations of feeling “under their proper heads” in that work. I begin by explaining his three highest-level distinctions bearing on this terrain: that between impressions and ideas; that between original impressions and secondary impressions; and that between the passions and the other emotions. In order to understand this third distinction, I explain his three different senses of the term ‘emotion’ and the relations among them. I ill then examine five different kinds of secondary impressions that he recognizes. These are: (1) sensible agitations; (2) feelings of or from mental operations; (3) volitions; (4) the passions (including motives); and (5) sentiments of taste. The broad outlines of the resulting geography are mapped out in a chart at the end. (shrink)

A Kuhnian reformulation of the recent debate in psychiatric nosography suggested that the current psychiatric classification system (the DSM) is in crisis and that a sort of paradigm shift is awaited (Aragona, 2009). Among possible revolutionary alternatives, the proposed fi ve-axes etiopathogenetic taxonomy (Charney et al., 2002) emphasizes the primacy of the genotype over the phenomenological level as the relevant basis for psychiatric nosography. Such a position is along the lines of the micro-reductionist perspective of E. Kandel (1998, 1999), (...) which sees mental disorders reducible to explanations at a fundamental epistemic level of genes and neurotransmitters. This form of micro-reductionism has been criticized as a form of genetic-molecular fundamentalism (e.g. Murphy, 2006) and a multi-level approach, in the form of the burgeoning Cognitive Neuropsychiatry, was proposed. This article focuses on multi-level mechanistic explanations, coming from Cognitive Science, as a possible alternative etiopathogenetic basis for psychiatric classification. The idea of a mechanistic approach to psychiatric taxonomy is here defended on the basis of a better conception of levels and causality. Nevertheless some critical remarks of Mechanism as a psychiatric general view are also offered. (shrink)

Jonathan Y. Tsou examines and defends positions on central issues in philosophy of psychiatry. The positions defended assume a naturalistic and realist perspective and are framed against skeptical perspectives on biological psychiatry. Issues addressed include the reality of mental disorders; mechanistic and disease explanations of abnormal behavior; definitions of mental disorder; natural and artificial kinds in psychiatry; biological essentialism and the projectability of psychiatric categories; looping effects and the stability of mental disorders; psychiatric classification; and the validity (...) of the DSM's diagnostic categories. The main argument defended by Tsou is that genuine mental disorders are biological kinds with harmful effects. This argument opposes the dogma that mental disorders are necessarily diseases that result from biological dysfunction. Tsou contends that the broader ideal of biological kinds offers a more promising and empirically ascertainable naturalistic standard for assessing the reality of mental disorders and the validity of psychiatric categories. (shrink)

Editor’s Note: If all goes as planned, the American Psychiatric Association will release a new Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in May 2013. Since 1980, the DSM has provided a shared diagnostic language to clinicians, patients, scientists, school systems, courts, and pharmaceutical and insurance companies; any changes to the influential manual will have serious ramifications. But, argues Dr. Steven Hyman, the DSM is a poor mirror of clinical and biological realities; a fundamentally new approach to diagnostic (...) classification is needed as researchers uncover novel ways to study and understand mental illness. (shrink)

Winner of the 2020 Essay Competition of the International Social Ontology Society. -/- Ian Hacking uses the looping effect to describe how classificatory practices in the human sciences interact with the classified people. While arguably this interaction renders the affected human kinds unstable and hence different from natural kinds, realists argue that also some prototypical natural kinds are interactive and human kinds in general are stable enough to support explanations and predictions. I defend a more fine-grained realist interpretation of interactive (...) human kinds by arguing for an explanatory domain account of the looping effect. First, I argue that knowledge of the feedback mechanisms that mediate the looping effect can supplement, and help to identify, the applicability domain over which a kind and its property variations are stably explainable. Second, by applying this account to cross-cultural case studies of psychiatric disorders, I distinguish between congruent feedback mechanisms that explain matches between classifications and kinds, and incongruent feedback mecha- nisms that explain mismatches. For example, congruent mechanisms maintain Western auditory experiences in schizophrenia, whereas exporting diagnostic labels inflicts incongruence by influencing local experiences. Knowledge of the mechanisms can strengthen explanatory domains, and thereby facilitate classificatory adjustments and possible interventions on psychiatric disorders. (shrink)

In this paper, I examine Reinforcement Learning modelling practice in psychiatry, in the context of alcohol use disorders. I argue that the epistemic roles RL currently plays in the development of psychiatric classification and search for explanations of clinically relevant phenomena are best appreciated in terms of Chang’s account of epistemic iteration, and by distinguishing mechanistic and aetiological modes of computational explanation.

While classifications of mental disorders have existed for over one hundred years, it still remains unspecified what terms such as 'mental disorder', 'disease' and 'illness' might actually denote. While ontologies have been called in aid to address this shortfall since the GALEN project of the early 1990s, most attempts thus far have sought to provide a formal description of the structure of some pre-existing terminology or classification, rather than of the corresponding structures and processes on the side of (...) the patient. We here present a view of mental disease that is based on ontological realism and which follows the principles embodied in Basic Formal Ontology and in the application of BFO in the Ontology of General Medical Science. We analyzed statements about what counts as a mental disease provided in the research agenda for the DSM-V, and in Pies' model. The results were used to assess whether the representational units of BFO and OGMS were adequate as foundations for a formal representation of the entities in reality that these statements attempt to describe. We then analyzed the representational units specific to mental disease and provided corresponding definitions. Our key contributions lie in the identification of confusions and conflations in the existing terminology of mental disease and in providing what we believe is a framework for the sort of clear and unambiguous reference to entities on the side of the patient that is needed in order to avoid these confusions in the future. (shrink)

Are psychiatric disorders natural kinds? This question has received a lot of attention within present-day philosophy of psychiatry, where many authors debate the ontology and nature of mental disorders. Similarly, historians of psychiatry, dating back to Foucault, have debated whether psychiatric researchers conceived of mental disorders as natural kinds or not. However, historians of psychiatry have paid little to no attention to the influence of (a) theories within logic, and (b) theories within metaphysics on psychiatric accounts of (...) proper method, and on accounts of the nature and classification of mental disorders. Historically, however, logic and metaphysics have extensively shaped methods and interpretations of classifications in the natural sciences. This paper corrects this lacuna in the history of psychiatry, and demonstrates that theories within logic and metaphysics, articulated by Christian Wolff (1679-1754), have significantly shaped the conception of medical method and (psychiatric) nosology of the influential nosologist Boissier De Sauvages (1706-1767). After treating Sauvages, I discuss the method of the influential nosologist William Cullen (1710-1790), and demonstrate the continuity between the classificatory methods of Sauvages and Cullen. I show that both Sauvages and Cullen were essentialists concerning medical diseases in general and psychiatric disorders in particular, contributing to the history of conceptions of the ontology and nature of mental disorders. (shrink)

Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether (...) a certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have (...) been proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at (...) various levels of AI development, namely, before it starts self-improvement, during its takeoff, when it uses various instruments to escape its initial confinement, or after it successfully takes over the world and starts to implement its goal system, which could be plainly unaligned, or feature-flawed friendliness. AI could also halt at later stages of its development either due to technical glitches or ontological problems. Overall, we identified around several dozen scenarios of AI-driven global catastrophe. The extent of this list illustrates that there is no one simple solution to the problem of AI safety, and that AI safety theory is complex and must be customized for each AI development level. (shrink)

Sign Language Recognition (SLR) aims to translate sign language into text or speech in order to improve communication between deaf-mute people and the general public. This task has a large social impact, but it is still difficult due to the complexity and wide range of hand actions. We present a novel 3D convolutional neural network (CNN) that extracts discriminative spatial-temporal features from photo datasets. This article is about classification of sign languages are not universal and are usually not mutually (...) intelligible although there are also similarities among different sign languages. They are the foundation of local Deaf cultures and have evolved into effective means of communication. Although signing is primarily used by the deaf and hard of hearing, hearing people also use it when they are unable to speak, when they have difficulty speaking due to a health condition or disability (augmentative and alternative communication), or when they have deaf family members, such as children of deaf adults. In this article we use the 43500 image in the dataset in size 64*64 pixel by use CNN Architecture and achieved 100% accuracy. (shrink)

„Errors are the greatest obstacles to the progress of science; to correct such errors is of more practical value than to achieve new knowledge,“ asserted Eugen Bleuler. Basic error of several prevailing classification schemes of pathological conditions, as for example ICD-10, lies in confusing and mixing symptoms with diseases, what makes them unscientific. Considering the need to bring order into the chaos and light into terminological obscureness, I introduce the Causal classification of diseases originating from the notion of (...) bodily wholeness and causality of its loss. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our (...) neural network, featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

A pineapple is a tropical plant with eatable leafy foods most monetarily critical plant in the family Bromeliaceous. The pineapple is native to South America, where it has been developed for a long time. The acquaintance of the pineapple with Europe in the seventeenth century made it a critical social symbol of extravagance. Since the 1820s, pineapple has been industrially filled in nurseries and numerous tropical manors. Further, it is the third most significant tropical natural product in world creation. In (...) the twentieth century, Hawaii was a prevailing maker of pineapples, particularly for the US, be that as it may, by 2016, Costa Rica, Brazil, and the Philippines represented almost 33% of the world's creation of pineapples. In this paper, machine learning based approach is presented for identifying type pineapple with a dataset that contains 1,311images use 946 images for training, 197 images for validation and 168 images for testing. A deep learning technique that extensively applied to image recognition was used. use 70% from image for training and 30% from image for validation. Our trained model achieved an accuracy of 100% on a heldout test set. (shrink)

American Sign Language, or ASL as its acronym is commonly known, is a fascinating language, and many people outside of the Deaf community have begun to recognize its value and purpose. It is a visual language consisting of coordinated hand gestures, body movements, and facial expressions. Sign language is not a universal language; it varies by country and is heavily influenced by the native language and culture. The American Sign Language alphabet and the British Sign Language alphabet are completely contrary. (...) Fingerspelling is one-handed in ASL and two-handed in BSL, with the exception of the letter C. AI technologies, particularly deep learning, can play an important role in breaking down communication barriers between deaf or hearing-impaired people and other communities, significantly contributing to their social inclusion. Recent advances in sensing technologies and AI algorithms have paved the way for the development of a wide range of applications aimed at meeting the needs of the deaf and hearing-impaired communities. To that end, the purpose of this paper is By attempting to translate sign language using artificial intelligence algorithms, we focused in this paper on a transfer learning technique based on deep learning using a ResNet algorithm and compared it to previous papers in which we used VGG19[1]and mobileNet [2], where we get in the ResNet algorithm on the degree of Accuracy 93,48% e number of images (43500 in the dataset in size 64*64 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this paper is (...) By attempting to translate sign language using deep learning models, we focused in this paper a comparing between Inception and Xception ¬¬¬¬¬ algorithm and compared it to previous papers in which we used VGG19[1], mobileNet [2], where we get in the Inception and Xception algorithm on the degree of Accuracy 99.32%, 99,43% respectively. number of images (29000 in the dataset in size 75*75 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

This paper proposes a classificatory framework for disjunctivism about the phenomenology of visual perceptual experience. Disjunctivism of this sort is typically divided into positive and negative disjunctivism. This distinction successfully reflects the disagreement amongst disjunctivists regarding the explanatory status of the introspective indiscriminability of veridical perception and hallucination. However, it is unsatisfactory in two respects. First, it cannot accommodate eliminativism about the phenomenology of hallucination. Second, the class of positive disjunctivism is too coarse-grained to provide an informative overview of the (...) current dialectical landscape. Given this, I propose a classificatory framework which preserves the positive-negative distinction, but which also includes the distinction between eliminativism and non-eliminativism, as well as a distinction between two subclasses of positive disjunctivism. In describing each class in detail, I specify who takes up each position in the existing literature, and demonstrate that this classificatory framework can disambiguate some existing disjunctivist views. (shrink)

In a world of ever growing specialization, the idea of a unity of science is commonly discarded, but cooperative work involving cross-disciplinary points of view is encouraged. The aim of this paper is to show with some textual support that Charles S. Peirce not only identified this paradoxical situation a century ago, but he also mapped out some paths for reaching a successful solution. A particular attention is paid to Peirce's classification of the sciences and to his conception of (...) science as a collective and cooperative activity of all those whose lives are animated by the desire to discover the truth. -/- . (shrink)

Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing accuracy 96% (...) .Testing dataset organized into one folder (age-gender-test) and contains 1676 audio files related to 39 of age and gender categories and 40 epoch. (shrink)

Discussions of psychiatric nosology focus on a few popular examples of disorders, and on the validity of diagnostic criteria. Looking at Anorexia Nervosa, an example rarely mentioned in this literature, reveals a new problem: the DSM has a strict taxonomic structure, which assumes that disorders can only be located on one branch. This taxonomic assumption fails to fit the domain of psychopathology, resulting in obfuscation of cross-category connections. Poor outcomes for treatment of Anorexia may be due to it (...) being pigeonholed as an Eating Disorder, when a disturbance of body perception may be a more central symptom than food restriction. This paper explores the possibility of restructuring the DSM taxonomy to allow for a pluralist classification of disorders. This change could improve treatment and research without requiring any changes to diagnostic criteria. (shrink)

This chapter proceeds in five steps. First, we will describe and justify the structure of the traditional system of species classification. Second, we will discuss three formal principles governing the development of taxonomies in general. It will emerge that, in addition to these formal principles, a division of living beings must meet certain empirical constraints. In the third section, we will show that the traditional division of living beings into species best meets these constraints. Fourth, we will argue that (...) a taxonomic system based on this notion of species provides a more natural alternative to the many arbitrary classifications that are possible. Hence, the traditional classificatory system is also the most natural one. Finally, we will discuss and reject an alternative account that suggests defining species solely with a view to their evolutionary history. We will argue that taxonomic trees do not depict hereditary connections but, rather, something else. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features derived from (...) medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

The scheme for classifications of concepts is introduced. It has founded on the triplet model of concepts. In this model a concept is depicted by means of three kinds of knowledge: a concept base, a concept representing part and the linkage between them. The idea of triplet classifications of concepts is connected with a usage of various specifications of these knowledge kinds as classification criteria.

the proclivity of many people to classify human acts as good or bad calls into mind the import of ETHICS. The penchant for classification warrants the evaluation of the bases for saying that one is bad or good action. Normally, human act is ethical if it is in accordance with what one would relatively expect in view of the events or the circumstances and unethical if the action is not called for by the circumstances, or a person whose behavior (...) is disorderly and inconsistent. The same observations harbor on perception rather than on paradigms and frameworks which ethicists have somehow perfected to classify bad behavior into one division and good behavior into another. The study of ETHICS will therefore increase proficiency at least in moral decision making. Likewise, knowledge of the course will give students certain techniques for evaluating others’ action as moral or immoral, including their own. ETHICS is the science of action but the action herein alluded to is not the action that concerns those that are unconscious and spontaneous. Action here is that resulting from intellect and will herein referred to as human act. ETHICS is both a theoretical and a practical discipline. The language of ethics refers to rights, duties, and values. One of the goals of ethics is to explore the nature of moral experience, its universality, and its diversity. Another is to provide intellectual analysis of values, and value conflicts in order to define man’s duties. Also, it is oriented toward the determination of right decisions. In order to do that, it is necessary to go step by step, analyzing, first, the facts of the case, second, the values at stake, and third, the duties. In short, ETHICS has the very practical purpose of helping us to choose, decide, and act morally. It should enable us to discover defects in the action of others and to avoid defects in our own action. (shrink)

Abstract—Smart homes and Wireless Home Automation Networks (WHAN) face several challenges in terms of cost, scalability, reliability, delay, energy consumption and many others. Smart homes typically have huge number of communicating devices. Efficient management of network resources is a major challenge in such environments. This paper provides insights on how to improve the MAC layer in smart home networks to fulfil the requirements of the different smart home applications. It provides a classification of the different smart home applications and (...) identifies the main requirements and challenges regarding the MAC layer in this environment. It also provides insights for MAC protocols designers by highlighting the main issues in designing MAC schemes for smart home environment. Based on the analysis, the paper highlights adaptability as the most critical and challenging feature for smart home MAC protocols. (shrink)

One of the crucial problems of argumentation schemes as illustrated in (Walton, Reed & Macagno 2008) is their practical use for the purpose of analyzing texts and producing arguments. The high number and the lack of a classification criterion make this instrument extremely difficult to apply practically. The purpose of this paper is to analyze the structure of argumentation schemes and outline a possible criterion of classification based on alternative and mutually-exclusive possibilities. Such a criterion is based not (...) on what an argument is, but how it can be understood and interpreted. The schemes are grouped according to an end-means principle, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

In his target article and recent interesting book about addiction and the brain, Marc Lewis claims that the prevalent medical view of addiction as a brain disease or a disorder, is mistaken. In this commentary we critically examine his arguments for this claim. We find these arguments to rest on some problematical and largely undefended assumptions about notions of disease, disorder and the demarcation between them and good health. Even if addiction does seem to differ from some typical brain diseases, (...) we believe contrary to Lewis, that there are still good reasons to maintain its classification as a mental or behavioral disorder. (shrink)